



'''simple docstring'''



import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch
@require_torch
class 						lowerCamelCase_				:







						@property
						def        __lowercase     (	self :   Dict					):

													"""simple docstring"""
													return self.get_dummy_input()







						@property
						def        __lowercase     (	self :   List[Any]					):

													"""simple docstring"""
													if self.block_type == "down":
																				return (4, 32, 16, 16)
													elif self.block_type == "mid":
																				return (4, 32, 32, 32)
													elif self.block_type == "up":
																				return (4, 32, 64, 64)

													raise ValueError(F"""'{self.block_type}' is not a supported block_type. Set it to 'up', 'mid', or 'down'."""					)







						def        __lowercase     (	self :   int					,     lowerCAmelCase__ :   Union[str, Any]=True					,     lowerCAmelCase__ :   Optional[int]=False					,     lowerCAmelCase__ :   Optional[int]=False					,     lowerCAmelCase__ :   Tuple=False					,     ):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      4
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      32
													SCREAMING_SNAKE_CASE       :   str         =      (32, 32)

													SCREAMING_SNAKE_CASE       :   Optional[int]         =      torch.manual_seed(0					)
													SCREAMING_SNAKE_CASE       :   List[str]         =      torch.device(lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   Tuple         =      (batch_size, num_channels) + sizes
													SCREAMING_SNAKE_CASE       :   int         =      randn_tensor(lowerCAmelCase__					,     generator=lowerCAmelCase__					,     device=lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   List[str]         =      {'''hidden_states''': hidden_states}

													if include_temb:
																				SCREAMING_SNAKE_CASE       :   List[str]         =      1_28
																				SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      randn_tensor((batch_size, temb_channels)					,     generator=lowerCAmelCase__					,     device=lowerCAmelCase__					)

													if include_res_hidden_states_tuple:
																				SCREAMING_SNAKE_CASE       :   Dict         =      torch.manual_seed(1					)
																				SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      (randn_tensor(lowerCAmelCase__					,     generator=lowerCAmelCase__					,     device=lowerCAmelCase__					),)

													if include_encoder_hidden_states:
																				SCREAMING_SNAKE_CASE       :   str         =      floats_tensor((batch_size, 32, 32)					).to(lowerCAmelCase__					)

													if include_skip_sample:
																				SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      randn_tensor(((batch_size, 3) + sizes)					,     generator=lowerCAmelCase__					,     device=lowerCAmelCase__					)

													return dummy_input







						def        __lowercase     (	self :   List[Any]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   str         =      {
													    '''in_channels''': 32,
													    '''out_channels''': 32,
													    '''temb_channels''': 1_28,
													}
													if self.block_type == "up":
																				SCREAMING_SNAKE_CASE       :   List[Any]         =      32

													if self.block_type == "mid":
																				init_dict.pop('''out_channels'''					)

													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      self.dummy_input
													return init_dict, inputs_dict







						def        __lowercase     (	self :   List[str]					,     lowerCAmelCase__ :   Union[str, Any]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   List[str]         =      self.prepare_init_args_and_inputs_for_common()
													SCREAMING_SNAKE_CASE       :   str         =      self.block_class(**lowerCAmelCase__					)
													unet_block.to(lowerCAmelCase__					)
													unet_block.eval()

													with torch.no_grad():
																				SCREAMING_SNAKE_CASE       :   str         =      unet_block(**lowerCAmelCase__					)

													if isinstance(lowerCAmelCase__					,     lowerCAmelCase__					):
																				SCREAMING_SNAKE_CASE       :   List[str]         =      output[0]

													self.assertEqual(output.shape					,     self.output_shape					)

													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      output[0, -1, -3:, -3:]
													SCREAMING_SNAKE_CASE       :   Dict         =      torch.tensor(lowerCAmelCase__					).to(lowerCAmelCase__					)
													assert torch_all_close(output_slice.flatten()					,     lowerCAmelCase__					,     atol=5e-3					)







						@unittest.skipIf(torch_device == '''mps'''					,     '''Training is not supported in mps'''					)
						def        __lowercase     (	self :   str					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   Tuple         =      self.prepare_init_args_and_inputs_for_common()
													SCREAMING_SNAKE_CASE       :   List[Any]         =      self.block_class(**lowerCAmelCase__					)
													model.to(lowerCAmelCase__					)
													model.train()
													SCREAMING_SNAKE_CASE       :   Tuple         =      model(**lowerCAmelCase__					)

													if isinstance(lowerCAmelCase__					,     lowerCAmelCase__					):
																				SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      output[0]

													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      torch.device(lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      randn_tensor(output.shape					,     device=lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   List[str]         =      torch.nn.functional.mse_loss(lowerCAmelCase__					,     lowerCAmelCase__					)
													loss.backward()









'''simple docstring'''



from queue import PriorityQueue
from typing import Any

import numpy as np
def        UpperCAmelCase (    A	:					dict       ,		A	:					str       ,		A	:					set       ,		A	:					set       ,		A	:					dict       ,		A	:					dict       ,		A	:					PriorityQueue       ,		A	:					dict       ,		A	:					float | int       ,		):
							for nxt, d in graph[v]:
														if nxt in visited_forward:
																					continue
														SCREAMING_SNAKE_CASE       :   str         =      cst_fwd.get(A       ,		np.inf					)
														SCREAMING_SNAKE_CASE       :   Optional[int]         =      cst_fwd[v] + d
														if new_cost_f < old_cost_f:
																					queue.put((new_cost_f, nxt)					)
																					SCREAMING_SNAKE_CASE       :   Tuple         =      new_cost_f
																					SCREAMING_SNAKE_CASE       :   List[str]         =      v
														if nxt in visited_backward:
																					if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
																												SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      cst_fwd[v] + d + cst_bwd[nxt]
							return shortest_distance
def        UpperCAmelCase (    A	:					str       ,		A	:					str       ,		A	:					dict       ,		A	:					dict					):
							SCREAMING_SNAKE_CASE       :   Dict         =      -1

							SCREAMING_SNAKE_CASE       :   Optional[int]         =      set()
							SCREAMING_SNAKE_CASE       :   Optional[Any]         =      set()
							SCREAMING_SNAKE_CASE       :   Any         =      {source: 0}
							SCREAMING_SNAKE_CASE       :   Tuple         =      {destination: 0}
							SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      {source: None}
							SCREAMING_SNAKE_CASE       :   Optional[int]         =      {destination: None}
							SCREAMING_SNAKE_CASE       :   PriorityQueue[Any]         =      PriorityQueue()
							SCREAMING_SNAKE_CASE       :   PriorityQueue[Any]         =      PriorityQueue()

							SCREAMING_SNAKE_CASE       :   List[str]         =      np.inf

							queue_forward.put((0, source)					)
							queue_backward.put((0, destination)					)

							if source == destination:
														return 0

							while not queue_forward.empty() and not queue_backward.empty():
														SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   str         =      queue_forward.get()
														visited_forward.add(A					)

														SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   int         =      queue_backward.get()
														visited_backward.add(A					)

														SCREAMING_SNAKE_CASE       :   Tuple         =      pass_and_relaxation(
														    A       ,		A       ,		A       ,		A       ,		A       ,		A       ,		A       ,		A       ,		A       ,		)

														SCREAMING_SNAKE_CASE       :   Optional[int]         =      pass_and_relaxation(
														    A       ,		A       ,		A       ,		A       ,		A       ,		A       ,		A       ,		A       ,		A       ,		)

														if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
																					break

							if shortest_distance != np.inf:
														SCREAMING_SNAKE_CASE       :   int         =      shortest_distance
							return shortest_path_distance


lowerCAmelCase_  :							int             =    {
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['G', 2]],
    'F': [],
    'G': [['F', 1]],
}
lowerCAmelCase_  :							str             =    {
    'B': [['E', 1]],
    'C': [['B', 1]],
    'D': [['C', 1]],
    'F': [['D', 1], ['G', 1]],
    'E': [[None, np.inf]],
    'G': [['E', 2]],
}

if __name__ == "__main__":
							import doctest

							doctest.testmod()




